Apparatus and method for recognizing emotion based on emotional segments

ABSTRACT

An apparatus and method to recognize a user&#39;s emotion based on emotional segments are provided. An emotion recognition apparatus includes a sampling unit configured to extract sampling data from input data for emotion recognition. The emotion recognition apparatus further includes a data segment creator configured to segment the sampling data into a plurality of data segments. The emotion recognition apparatus further includes an emotional segment creator configured to create a plurality of emotional segments that include a plurality of emotions corresponding to each of the respective data segments.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit under 35 U.S.C. §119(a) of a KoreanPatent Application No. 10-2011-0121173, filed on Nov. 18, 2011, in theKorean Intellectual Property Office, the entire disclosure of which isincorporated herein by reference for all purposes.

BACKGROUND

1. Field

The following description relates to an apparatus and method torecognize a user's emotion and provide an appropriate service accordingto the user's emotion.

2. Description of the Related Art

A user's emotion has been estimated based on direct information, such asthe user's look or voice, from which the user's emotion can be directlyrecognized. In addition, a user's emotion has been estimated based onindirect information, such as the user's behavior pattern.

In recognizing a user's emotion at a specific time, a sufficient amountof successive data may be essential for a more exact recognition.However, there may be difficulties in accurately interpreting a user'semotional state from given data since a human's emotions tend to becomecomplicated and often change. Moreover, data about successivedirect/indirect human behaviors can include certain noise that may causeperformance deterioration in emotion recognition.

SUMMARY

In one general aspect, there is provided an emotion recognitionapparatus including a sampling unit configured to extract sampling datafrom input data for emotion recognition. The emotion recognitionapparatus further includes a data segment creator configured to segmentthe sampling data into a plurality of data segments. The emotionrecognition apparatus further includes an emotional segment creatorconfigured to create a plurality of emotional segments that include aplurality of emotions corresponding to each of the respective datasegments.

The data segment creator is further configured to segment the samplingdata at regular intervals based on a predetermined time-domain window.

The data segment creator is further configured to segment the samplingdata at different intervals based on two or more predeterminedtime-domain windows.

The data segment creator is further configured to reconfigure thesampling data based on a predetermined time-domain window such that aportion of the sampling data is shared by at least two of the datasegments.

An emotion deciding unit configured to decide at least one of a user'srepresentative emotion, a user's complex emotion, and a user's changedemotion based on the emotional segments.

The emotional segment creator is further configured to calculate aplurality of probability values of a plurality of candidate emotions foreach of the respective data segments, and extract a candidate emotionhaving a greatest probability value from among the candidate emotions todecide the extracted candidate emotion as an emotional segment of acorresponding data segment, for each of the respective data segments.

An emotion deciding unit configured to decide at least one of a user'srepresentative emotion, a user's complex emotion, and a user's changedemotion based on the emotional segment.

The emotion deciding unit further includes a representative emotiondeciding unit configured to decide at least one of the emotionalsegments as the user's representative emotion based on a degree ofreliability of each of the respective emotional segments.

The degree of reliability includes at least one of a standard deviationand an entropy value of the probability values for a respective one ofthe data segments.

The emotion deciding unit further includes a complex emotion decidingunit configured to decide at least two of the emotional segments as theuser's complex emotion based on a degree of reliability of each of therespective emotional segments.

The degree of reliability of each emotional segment is defined based onat least one of a standard deviation and an entropy value of theprobability values for a respective one of the data segments.

The emotion deciding unit further includes a changed emotion decidingunit configured to decide a time-ordered arrangement of the emotionalsegments as the changed emotion.

In another general aspect, there is provided an emotion recognitionmethod including extracting sampling data from input data for emotionrecognition. The emotion recognition method further includes segmentingthe sampling data into a plurality of data segments. The emotionrecognition method further includes creating a plurality of emotionalsegments that include a plurality of emotions corresponding to each ofthe respective data segments.

The emotion recognition method further includes deciding at least one ofa user's representative emotion, a user's complex emotion, and a user'schanged emotion based on the emotional segments.

The emotion recognition method further includes calculating a pluralityof probability values of a plurality of candidate emotions for each ofthe respective data segments.

The emotion recognition method further includes calculating at least oneof a plurality of standard deviations and a plurality of entropy valuesof the probability values for each of the respective data segments.

The emotion recognition method further includes extracting a greateststandard deviation from the standard deviations, and/or a greatestentropy value from among the entropy values. The emotion recognitionmethod further includes deciding an emotional segment of a data segmentcorresponding to at least one of the greatest standard deviation and thegreatest entropy value as a user's representative emotion.

The emotion recognition method further includes extracting a standarddeviation greater than a predetermined threshold from among the standarddeviations, and/or an entropy value greater than a predeterminedthreshold from among the entropy values. The emotion recognition methodfurther includes deciding an emotional segment of a data segmentcorresponding to at least one of the extracted standard deviation andthe extracted entropy value as a user's representative emotion.

In yet another general aspect, there is provided an apparatus includinga data segment generator configured to generate data segments based oninput data for emotion recognition. The apparatus further includes anemotional segment generator configured to generate emotional segmentsthat include emotions corresponding to the respective data segments. Theapparatus further includes an emotion recognition controller configuredto recognize an emotion of a user based on the emotional segments.

The apparatus further includes a sampling data generator configured togenerate sampling data based on the input data. The data segmentgenerator is further configured to generate the data segments based onthe sampling data.

The emotion segment generator is further configured to determineprobability values of candidate emotions for each of the respective datasegments. The emotion recognition controller is further configured todetermine at least one of standard deviations and entropy values of theprobability values for each of the respective data segments.

The emotion recognition controller is further configured to recognize atleast two of the emotional segments as a user's complex emotion based onpredetermined criteria associated with at least one of the probabilityvalues, the standard deviations, and the entropy values.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example of an emotion recognitionapparatus.

FIG. 2 is a waveform graph illustrating examples of input data andsampling data.

FIGS. 3A through 3C are waveform graphs illustrating examples of datasegments.

FIG. 4 is a table illustrating an example of an emotional segmentcreating method.

FIG. 5 is a diagram illustrating another example of an emotionrecognition apparatus.

FIG. 6 is a diagram illustrating an example of an emotion deciding unit.

FIGS. 7A through 7C are tables illustrating an example of an emotiondeciding method that is performed by the emotion deciding unit.

FIG. 8 is a flowchart illustrating an example of an emotion recognitionmethod based on emotional segments.

Throughout the drawings and the detailed description, unless otherwisedescribed, the same drawing reference numerals will be understood torefer to the same elements, features, and structures. The relative sizeand depiction of these elements may be exaggerated for clarity,illustration, and convenience.

DETAILED DESCRIPTION

The following description is provided to assist the reader in gaining acomprehensive understanding of the methods, apparatuses, and/or systemsdescribed herein. Accordingly, various changes, modifications, andequivalents of the methods, apparatuses, and/or systems described hereinwill be suggested to those of ordinary skill in the art. Also,descriptions of well-known functions and constructions may be omittedfor increased clarity and conciseness.

FIG. 1 is a diagram illustrating an example of an emotion recognitionapparatus 100. The emotion recognition apparatus 100 may include asensor 101, a sampling unit 102, a data segment creator 103, and anemotional segment creator 104.

The sensor 101 may measure and collect various kinds of input data torecognize a user's emotion. The input data may include various kinds oftime-domain data related to the user. For example, the input data mayinclude the user's facial image, the user's voice, text input by theuser, the user's temperature, the user's location, a kind of anapplication being used by the user, etc. These input data are examples,and other input data may be used depending on the particular situation.

The sampling unit 102 may extract sampling data from the input dataacquired by the sensor 101. For example, the sampling unit 102 mayextract a portion of time-domain input data acquired by the sensor 101,using a predetermined size of a time-domain window. This process will bedescribed in more detail with reference to FIG. 2 below.

FIG. 2 is a waveform graph illustrating examples of input data 200 andsampling data S. The input data 200 acquired by the sensor 101 in FIG. 1may be transferred to the sampling unit 102 in FIG. 1. For example, ifthe input data 200 is a voice signal, the X-axis may be a time (t), andthe Y-axis may be a magnitude of the voice signal. The sampling unit 102may extract the sampling data S in correspondence to a time period froma time point T0 to a time point T5, using a predetermined size of atime-domain window. The time period during which the sampling data S isextracted, a magnitude of the sampling data S, and the like, may varydepending on the purpose of an application.

Returning again to FIG. 1, the data segment creator 103 may segment theextracted sampling data S into a plurality of data segments. A datasegment may be a portion or all of the sampling data S. For example, thedata segment creator 103 may segment or reconfigure the sampling data Susing a time-domain window having a size smaller than or equal to thesampling data S. In more detail, the data segment creator 103 may use atime-domain window to segment the sampling data S at regular intervals,or may use a plurality of time-domain windows having different sizes tosegment the sampling data at different intervals. As another example,the data segment creator 103 may segment or reconfigure the samplingdata S such that a portion of the sampling data S is shared by at leasttwo data segments. These examples will be described in more detail withreference to FIGS. 3A, 3B, and 3C below.

FIGS. 3A through 3C are waveform graphs illustrating examples of datasegments. Referring to FIGS. 1 and 3A, the data segment creator 103 maysegment sampling data 300 at regular intervals to create a plurality ofdata segments S1 through S5 having the same size. For example, the datasegment creator 103 may segment the sampling data 300 periodicallyduring a time period from a time point T0 to a time point T5.

Referring to FIGS. 1 and 3B, the data segment creator 103 may segmentthe sampling data 300 at different intervals to create a plurality ofdata segments 51 through S5 having different sizes. For example, thedata segment creator 103 may segment the sampling data 300non-periodically during the time period from T0 to T5. FIG. 3B shows thecase where the five data segments S1 through S5 all have differentsizes, and is only an example. That is, in other examples, some of thedata segments S1 through S5 may have the same size.

Referring to FIGS. 1 and 3C, the data segment creator 103 may createdata segments S1 through S5 using an overlapping time-domain window suchthat a portion of sampling data 300 is shared by at least two datasegments. For example, a time period corresponding to the data segmentS1 may exist in the other data segments S2 through S5. In the currentexample, the overlapping time-domain window may represent two or moretime-domain windows that share a specific time period. In other words,the data segment creator 103 may extract sampling data corresponding toa time period from T0 to T1 as a data segment S1, and againaccumulatively extract sampling data corresponding to a time period fromT0 to T2 as a data segment S2. FIG. 3C illustrates the case where thedata segments S2 through S5 have sizes of integer multiples of the datasegment S1. This is only an example, and it may be also possible to setmore various sizes of time-domain windows. As such, the data segmentcreator 103 may segment the sampling data 300 using a non-overlappingtime-domain window, or accumulatively segment the sampling data 300using an overlapping time-domain window, to thereby represent orreconfigure the sampling data 300.

Returning again to FIG. 1, the emotional segment creator 104 may createan emotional segment for each data segment. The emotional segment mayinclude an emotion (e.g., “Anger”, “Happiness”, etc.) corresponding toeach data segment. The emotional segment creator 104 may recognize ordetect emotional information corresponding to each data segment based onvarious methods. For example, the emotional segment creator 104 maystochastically decide emotional information (that is, an emotionalsegment) about each data segment based on degrees of similarity betweeneach data segment and predetermined emotional models. This process willbe described in more detail with reference to FIG. 4 below.

FIG. 4 is a table illustrating an example of an emotional segmentcreating method. The emotional segment creator 104 in FIG. 1 maystochastically decide an emotion corresponding to each data segment S1through S5, e.g., in FIG. 3C. For example, the data segment S1 may becalculated to have probability values of 51%, 67%, 59%, 44%, 31%, 50%,and 48% in correspondence to candidate emotions of “Neutral”, “Anger”,“Happiness”, “Surprise”, “Sadness”, “Disgust”, and “Fear”, respectively.Here, the probability value of each candidate emotion may be calculatedbased on a degree of similarity between the data segment S1 andpredetermined model data about the candidate emotion, e.g., a“Happiness” facial image group including representative expressions thattend to appear when humans are in a happy emotional state. The specificpercentages and emotions listed above are only examples, and otherpercentages and emotions may be used depending on the particularsituation.

After the probability values for the individual kinds of emotions areall calculated, the emotional segment creator 104 may decide the emotionhaving the greatest probability value as an emotional segment of thecorresponding data segment. For example, an emotional segment of thedata segment Si may be decided as “Anger”. This method of stochasticallydeciding the emotion is only an example, and emotional information ofeach data segment may be decided by a method of using a pattern or ruleof input values, a method of using a neutral network, etc.

Returning again to FIG. 1, the emotional recognition apparatus 100 maybe implemented as an electrical circuit and/or hardware that isinstalled in a specific terminal, and/or as a processor module or anapplication program that is included or installed in a specificterminal. The terminal may include, for example, a mobile terminal, suchas a smart phone, or a fixed terminal, such as a personal computer.Also, the blocks illustrated in FIG. 1 may be functional blocksclassified according to their logical functions. Accordingly, thefunctions of the emotion recognition apparatus 100 may be classifiedaccording to different criterion. Further, the functional blocks may beseparately implemented, or two or more of the functional blocks may beintegrated. Furthermore, a portion of functions of a functional blockmay be performed by one or more other functional blocks.

FIG. 5 is a diagram illustrating another example of an emotionrecognition apparatus 500. The emotion recognition apparatus 500 mayinclude the sensor 101, the sampling unit 102, the data segment creator103, the emotional segment creator 104, a model storage 501, and anemotion deciding unit 502. Here, the sensor 101, the sampling unit 102,the data segment creator 103, and the emotional segment creator 104 maybe the same as those described above in FIG. 1, and accordingly,detailed descriptions thereof will be omitted.

In FIG. 5, the model storage 501 may store predetermined data modeledaccording to a type of each emotion. For example, if the emotionrecognition apparatus 500 uses a user's facial image, the model storage501 may store a “sad” facial image group including representativeexpressions that tend to appear when humans are in a sad emotionalstate, a “happiness” facial image group including representativeexpressions that tend to appear when humans are in a happy emotionalstate, etc. Accordingly, the emotional segment creator 104 may compareeach data segment to the modeled data to calculate a degree (e.g., aprobability value) of similarity between the data segment and themodeled data. Also, after a user's emotion is decided by the emotiondeciding unit 502, the model storage 501 may learn the decided emotionand update the modeled data.

The emotion deciding unit 502 may decide at least one of the user'srepresentative emotion, the user's complex emotion, and the user'schanged emotion based on a plurality of emotional segments obtained bythe emotional segment creator 104. That is, since a human's emotions arecomplicated and change rapidly, the emotion deciding unit 502 may decideone of the emotional segments as the representative emotion, or two ormore of the emotional segments as the complex emotion. Also, the emotiondeciding unit 502 may arrange the emotional segments in an order of time(i.e., a series) to decide the changed emotion (that is, a change inemotion). The deciding of the representative emotion, the complexemotion, and the changed emotion will be described in more detail withreference to FIGS. 7A through 7C below.

FIG. 6 is a diagram illustrating an example of an emotion deciding unit600. The emotion deciding unit 600 may include a representative emotiondeciding unit 601, a complex emotion deciding unit 602, and a changedemotion deciding unit 603. FIG. 6 shows, for convenience of description,an example where the emotion deciding unit 600 includes three functionalblocks, and it may be also possible that only one or two of thefunctional blocks are selectively provided since the functional blocksoperate independently from each other.

The representative emotion deciding unit 601 may decide at least oneemotional segment as a representative emotion of sampling data accordingto a degree of reliability of each emotional segment. The complexemotion deciding unit 602 may decide at least two emotional segments asa complex emotion of the sampling data according to the degree ofreliability of each emotional segment. The degree of reliability of eachemotional segment may include a standard deviation or entropy ofemotional probability values corresponding to each data segment. Thechanged emotion deciding unit 603 may decide a time-ordered series ofemotional segments as a changed emotion. The deciding of therepresentative emotion, the complex emotion, and the changed emotionwill be described in more detail with reference to FIGS. 7A through 7Cbelow.

FIGS. 7A through 7C are tables illustrating an example of an emotiondeciding method that is performed by the emotion deciding unit 600.Referring to FIGS. 6 and 7A, the representative emotion deciding unit601 may calculate standard deviations (e.g., window confidences) ofemotional probability values for each data segment 51 through S5. Forexample, the standard deviation of 0.23 may be calculated for theemotional probability values of the data segment S3. The specificstandard deviations mentioned above and in FIG. 7A are only examples,and other standard deviations may be calculated depending on theparticular situation.

The representative emotion deciding unit 601 may extract the greateststandard deviation, or the standard deviation greater than apredetermined threshold value, from among the calculated standarddeviations. The representative emotion deciding unit 601 may decide anemotional segment of a data segment corresponding to the extractedstandard deviation as a user's representative emotion. For example, ifthe data segment S3 has the greatest standard deviation (e.g., 0.23),the representative emotion deciding unit 601 may decide the emotionalsegment “Neutral” of the data segment S3 as the corresponding user'srepresentative emotion. The decided representative emotion “Neutral” maybe decided as the user's representative emotion during a time periodfrom T0 to T5 in the example of, e.g., FIG. 2. In other words, the usermay be considered to be in a “Neutral” emotional state during the timeperiod from T0 to T5.

Referring to FIGS. 6 and 7B, the representative emotion deciding unit601 may calculate entropy values of emotional probability values foreach data segment 51 through S5. For example, the entropy values of 0.42may be calculated for the emotional probability values of the datasegments S3 through S5. The specific entropy values mentioned above andin FIG. 7B are only examples, and other entropy values may be calculateddepending on the particular situation.

The representative emotion deciding unit 601 may extract the greatestentropy value, or the entropy value greater than a predeterminedthreshold value, from among the calculated entropy values. Therepresentative emotion deciding unit 601 may decide an emotional segmentof a data segment corresponding to the extracted entropy value as auser's representative emotion. For example, “Neutral is therepresentative emotional segment of the data segment S3 having thegreatest entropy value (e.g., 0.42), and thus, may be decided as theuser's representative emotion. The decided representative emotion may bedecided as the user's representative emotion during the time period fromT0 to T5 in the example of, e.g., FIG. 2. That is, the user may beconsidered to be in a “Neutral” emotional state during the time periodfrom T0 to T5.

In another example, “Neutral”, “Sadness”, and “Disgust” are theemotional segments of the data segments S3, S4, and S5, respectively,whose entropy values (e.g., 0.42) are greater than a predeterminedthreshold value 0.3, and thus, any one of these emotional segments maybe decided as the user's representative emotion. The decidedrepresentative emotion may be s decided as the user's representativeemotion during the time period from T0 to T5 in the example of, e.g.,FIG. 2. For example, if “Disgust” is selected, the user may beconsidered to be in a “Disgust” emotional state during the time periodfrom T0 to T5.

Referring to FIGS. 6 and 7C, the complex emotion deciding unit 602 maycalculate standard deviations (e.g., window confidences) of emotionalprobability values for each data segment S1 through S5. For example, thestandard deviation of 0.23 may be calculated for the emotionalprobability values of the data segment S3. The specific standarddeviations mentioned above and in FIG. 7C are only examples, and otherstandard deviations may be calculated depending on the particularsituation.

The complex emotion deciding unit 602 may decide at least two emotionalsegments of the respective data segments 51 through S5 as a user'scomplex emotion according to predetermined criteria. For example, if thepredetermined criteria is a standard deviation greater than or equal to0.18 and a probability value greater than 80%, the emotional segments“Disgust” and “Neutral”, whose probability values are greater than 80%,of the respective data segments S2 and S3, may be decided as the user'scomplex emotion. The current example relates to the case where thecomplex emotion deciding unit 602 uses standard deviations to decide acomplex emotion. It may be also possible that the complex emotiondeciding unit 602 uses entropy values or various kinds of informationrelating to a degree of reliability of each emotional probability valueto decide a complex emotion. The decided emotional segments may bedecided as the user's complex emotion during the time period from T0 toT5 in the example of, e.g., FIG. 2. In other words, the user may beconsidered to be in a complex emotional state where “Disgust” and“Neutral” are mixed during the time period from T0 to T5.

In another example, the changed emotion deciding unit 603 may arrange aplurality of emotional segments in an order of time (i.e., a series) tothereby decide a changed emotion that represents changes in a user'semotion. For example, if the data segment creator 103 of FIG. 1 createsa plurality of data segments S1 through S5 as shown in, e.g., FIG. 3C,changes in the user's emotion may be represented as “Anger”, “Disgust”,“Neutral”, “Sadness”, “Disgust”, etc., during a time period from T0 toT5. That is, the user's emotion may be considered to change in an orderof “Anger”, “Disgust”, Neutral“, “Sadness”, and “Disgust” during thetime period from T0 to T5 in the example of, e.g., FIG. 2.

FIG. 8 is a flowchart illustrating an example of an emotion recognitionmethod based on emotional segments. At step 801, the emotionalrecognition apparatus 500 in FIG. 5 may extract sampling data from inputdata. For example, the sampling unit 102 in FIG. 5 may is extract thesampling data as shown in FIG. 2.

At step 802, the emotion recognition apparatus 500 may segment theextracted sampling data into a plurality of data segments. For example,the data segment creator 103 in FIG. 5 may segment and reconfigure thesampling data into the plurality of data segments as illustrated inFIGS. 3A through 3C.

At step 803, the emotion recognition apparatus 500 may create emotionalsegments in correspondence to each data segment. For example, theemotional segment creator 104 in FIG. 5 may stochastically decide theemotions for each data segment, as shown in FIG. 4.

At step 804, the emotion recognition apparatus 500 may decide arepresentative emotion, a complex emotion, or a changed emotion. Forexample, the emotion deciding unit 502 in FIG. 5 may decide therepresentative emotion, the complex emotion, the changed emotion, etc.,using the emotional segments as shown in FIGS. 7A through 7C.

Therefore, according to the examples as described above, there isprovided an apparatus and method to recognize a user's emotion based onemotional segments, which may more efficiently and correctly interpret ahuman's complex emotion. Accordingly, an appropriate service may beprovided according to the user's emotional state.

Program instructions to perform a method described herein, or one ormore operations thereof, may be recorded, stored, or fixed in one ormore computer-readable storage media. The program instructions may beimplemented by a computer. For example, the computer may cause aprocessor to execute the program instructions. The media may include,alone or in combination with the program instructions, data files, datastructures, and the like. Examples of non-transitory computer-readablestorage media include magnetic media, such as hard disks, floppy disks,and magnetic tape; optical media such as CD ROM disks and DVDs;magneto-optical media, such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory (ROM), random access memory (RAM), flash memory, andthe like. Examples of program instructions include machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter. The programinstructions, that is, software, may be distributed over network coupledcomputer systems so that the software is stored and executed in adistributed fashion. For example, the software and data may be stored byone or more computer readable storage mediums. Also, functionalprograms, codes, and code segments for accomplishing the exampleembodiments disclosed herein can be easily construed by programmersskilled in the art to which the embodiments pertain based on and usingthe flow diagrams and block diagrams of the figures and theircorresponding descriptions as provided herein. Also, the described unitto perform an operation or a method may be hardware, software, or somecombination of hardware and software. For example, the unit may be asoftware package running on a computer or the computer on which thatsoftware is running.

As a non-exhaustive illustration only, a terminal described herein mayrefer to mobile devices such as a cellular phone, a personal digitalassistant (PDA), a digital camera, a portable game console, and an MP3player, a portable/personal multimedia player (PMP), a handheld e-book,a portable laptop PC, a global positioning system (GPS) navigation, atablet, a sensor, and devices such as a desktop PC, a high definitiontelevision (HDTV), an optical disc player, a setup box, a homeappliance, and the like that are capable of wireless communication ornetwork communication consistent with that which is disclosed herein.

A number of examples have been described above. Nevertheless, it will beunderstood that various modifications may be made. For example, suitableresults may be achieved if the described techniques are performed in adifferent order and/or if components in a described system,architecture, device, or circuit are combined in a different mannerand/or replaced or supplemented by other components or theirequivalents. Accordingly, other implementations are within the scope ofthe following claims.

What is claimed is:
 1. An emotion recognition apparatus comprising: asampling unit configured to extract sampling data from input data foremotion recognition; a data segment creator configured to segment thesampling data into a plurality of data segments; and an emotionalsegment creator configured to create a plurality of emotional segmentsthat comprise a plurality of emotions corresponding to each of therespective data segments.
 2. The emotion recognition apparatus of claim1, wherein the data segment creator is further configured to segment thesampling data at regular intervals based on a predetermined time-domainwindow.
 3. The emotion recognition apparatus of claim 1, wherein thedata segment creator is further configured to segment the sampling dataat different intervals based on two or more predetermined time-domainwindows.
 4. The emotion recognition apparatus of claim 1, wherein thedata segment creator is further configured to reconfigure the samplingdata based on a predetermined time-domain window such that a portion ofthe sampling data is shared by at least two of the data segments.
 5. Theemotion recognition apparatus of claim 1, further comprising an emotiondeciding unit configured to decide at least one of a user'srepresentative emotion, a user's complex emotion, and a user's changedemotion based on the emotional segments.
 6. The emotion recognitionapparatus of claim 1, wherein the emotional segment creator is furtherconfigured to: calculate a plurality of probability values of aplurality of candidate emotions for each of the respective datasegments, and extract a candidate emotion having a greatest probabilityvalue from among the candidate emotions to decide the extractedcandidate emotion as an emotional segment of a corresponding datasegment, for each of the respective data segments.
 7. The emotionrecognition apparatus of claim 6, further comprising an emotion decidingunit configured to decide at least one of a user's representativeemotion, a user's complex emotion, and a user's changed emotion based onthe emotional segment.
 8. The emotion recognition apparatus of claim 7,wherein the emotion deciding unit further comprises a representativeemotion deciding unit configured to decide at least one of the emotionalsegments as the user's representative emotion based on a degree ofreliability of each of the respective emotional segments.
 9. The emotionrecognition apparatus of claim 8, wherein the degree of reliabilitycomprises at least one of a standard deviation and an entropy value ofthe probability values for a respective one of the data segments. 10.The emotion recognition apparatus of claim 7, wherein the emotiondeciding unit further comprises a complex emotion deciding unitconfigured to decide at least two of the emotional segments as theuser's complex emotion based on a degree of reliability of each of therespective emotional segments.
 11. The emotion recognition apparatus ofclaim 10, wherein the degree of reliability of each emotional segment isdefined based on at least one of a standard deviation and an entropyvalue of the probability values for a respective one of the datasegments.
 12. The emotion recognition apparatus of claim 7, wherein theemotion deciding unit further comprises a changed emotion deciding unitconfigured to decide a time-ordered arrangement of the emotionalsegments as the changed emotion.
 13. An emotion recognition methodcomprising: extracting sampling data from input data for emotionrecognition; segmenting the sampling data into a plurality of datasegments; and creating a plurality of emotional segments that comprise aplurality of emotions corresponding to each of the respective datasegments.
 14. The emotion recognition method of claim 13, furthercomprising deciding at least one of a user's representative emotion, auser's complex emotion, and a user's changed emotion based on theemotional segments.
 15. The emotion recognition method of claim 13,further comprising calculating a plurality of probability values of aplurality of candidate emotions for each of the respective datasegments.
 16. The emotion recognition method of claim 15, furthercomprising calculating at least one of a plurality of standarddeviations and a plurality of entropy values of the probability valuesfor each of the respective data segments.
 17. The emotion recognitionmethod of claim 16, further comprising: extracting a greatest standarddeviation from the standard deviations, and/or a greatest entropy valuefrom among the entropy values; and deciding an emotional segment of adata segment corresponding to at least one of the greatest standarddeviation and the greatest entropy value as a user's representativeemotion.
 18. The emotion recognition method of claim 16, furthercomprising: extracting a standard deviation greater than a predeterminedthreshold from among the standard deviations, and/or an entropy valuegreater than a predetermined threshold from among the entropy values;and deciding an emotional segment of a data segment corresponding to atleast one of the extracted standard deviation and the extracted entropyvalue as a user's representative emotion.
 19. An apparatus comprising: adata segment generator configured to generate data segments based oninput data for emotion recognition; an emotional segment generatorconfigured to generate emotional segments that comprise emotionscorresponding to the respective data segments; and an emotionrecognition controller configured to recognize an emotion of a userbased on the emotional segments.
 20. The apparatus of claim 19, furthercomprising: a sampling data generator configured to generate samplingdata based on the input data, wherein the data segment generator isfurther configured to generate the data segments based on the samplingdata.
 21. The apparatus of claim 19, wherein: the emotion segmentgenerator is further configured to determine probability values ofcandidate emotions for each of the respective data segments; and theemotion recognition controller is further configured to determine atleast one of standard deviations and entropy values of the probabilityvalues for each of the respective data segments.
 22. The apparatus ofclaim 21, wherein the emotion recognition controller is furtherconfigured to recognize at least two of the emotional segments as auser's complex emotion based on predetermined criteria associated withat least one of the probability values, the standard deviations, and theentropy values.